Climatic conditions at provenance origin influence growth stability to changes in climate in two major tree species
Bibliographic record
Abstract
Climate change is expected to outpace the rate at which populations of forest trees can migrate. Hence, in forestry there is growing interest in intervention strategies such as assisted migration to mitigate climate change impacts. However, until now the primary focus when evaluating candidates for assisted migration has been mean or maximum performance. We explore phenotypic plasticity as a potentially new avenue to help maintain the viability of species and populations in the face of climate change. Capitalizing on large, multi-site international provenance trials of four economically and ecologically important forest tree species (Fagus sylvatica, Picea abies, Picea engelmannii, Pinus contorta), we quantify growth stability as the width of the response function relating provenance growth performance and trial site climate. We found significant differences in growth stability among species, with P. engelmannii being considerably more stable than the other three species. Additionally, we found no relationship between growth performance and growth stability of provenances, indicating that there are fast-growing provenances with a broad climate optimum. In two of the four species, provenances’ growth stability showed a significant relationship with the climate of the seed source, the direction of which depends on the species. When taken together with data on growth performance in different climate conditions, a measure of growth stability can improve the choice of species and provenances to minimize future risks in forest restoration and reforestation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".